Quantification of Optical Clarity of Transparent Soil Using the Modulation Transfer Function
Bibliographic record
Abstract
Abstract Transparent synthetic soils have been developed as a soil surrogate to enable internal visualization of geotechnical processes in physical models. Transparency of the soil dictates the overarching success of the technique; however, despite this fundamental requirement, no quantitative framework has yet been established to appraise the visual quality of transparent soil. Previous approaches to assess and optimize transparency quality included an eye chart assessment method, although this approach is highly subjective and operator-dependent. In this paper, an independent method for quantitatively assessing the optical quality of transparent soil is proposed based on the optical calibration method, Modulation Transfer Function (MTF). The work explores this hypothesis and assesses the potential for MTF to quantify the optical quality of transparent soils for a number of aspects including (i) optimum oil blend ratio, (ii) depth of viewing plane, and (iii) temperature. The results confirmed that MTF offers a robust and reliable method to provide an independent quantitative measure of the optical quality of transparent soil. The impact of reduced soil transparency and the ability to track speckle patterns—thus accuracy and precision of displacement measurement—was correlated with MTF to evaluate the permissible viewing depth of transparent soil.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".